Fast Algorithm for Depth Map Intra-Frame Coding 3D-HEVC Based on Swin Transformer and Multi-Branch Network

Three-Dimensional High-Efficiency Video Coding (3D-HEVC) efficiently compresses 3D video by incorporating depth map encoding techniques. However, the quadtree partitioning of depth map coding units (CUs) greatly increases computational complexity, contributing to over 90% of the total encoding time. To overcome the limitations of existing methods in complex edge modeling and partitioning efficiency, this paper presents Swin-Hier Net, a hierarchical CU partitioning prediction model based on the Swin Transformer. First, a multi-branch feature fusion architecture is designed: the Swin Transformer's shifted window attention mechanism extracts global contextual features, lightweight CNNs capture local texture details, and traditional edge/variance features enhance multi-scale representation. Second, a recursive hierarchical decision mechanism dynamically activates sub-CU prediction branches based on the partitioning probability of parent nodes, ensuring strict compliance with the HEVC standard quadtree syntax. Additionally, a hybrid pooling strategy and dilated convolutions improve edge feature retention. Experiments on 3D-HEVC standard test sequences show that, compared to exhaustive traversal methods, the proposed algorithm reduces encoding time by 72.7% on average, lowers the BD-Rate by 1.16%, improves CU partitioning accuracy to 94.5%, and maintains a synthesized view PSNR of 38.68 dB (baseline: 38.72 dB). The model seamlessly integrates into the HTM encoder, offering an efficient solution for real-time 3D video applications.